Early warning system and method for avoiding fatigue operation

Abstract

The invention provides an early warning system and method for avoiding fatigue operation, and relates to the technical field of computers. The data collection module is used for collecting early warning data of operators; the eye state data accuracy evaluation module is used for analyzing the accuracy of the two groups of eye state evaluation data according to an eye state evaluation data accuracy index; the physiological state data accuracy evaluation module is used for analyzing the accuracy of the two groups of physiological state evaluation data according to physiological state evaluation data accuracy indexes; the early warning data accuracy evaluation module is used for obtaining early warning data accuracy evaluation indexes of the operators; and the analysis module analyzes the accuracy of the early warning data according to the early warning data accuracy evaluation index and performs early warning, so that the effect of accurately performing early warning on the fatigue state of the operator is achieved, and the problem that effective early warning data of the operator is difficult to accurately and timely obtain in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to an early warning system and method for avoiding fatigue work. Background Art

[0002] In modern society, many positions require workers to concentrate for long periods of time, and even involve working across the day and night. Especially when flying, driving, or operating complex equipment, long hours of work not only consumes mental energy, but is also accompanied by considerable physical exertion, which will reduce the workers' alertness and reaction speed, causing a decline in work ability. High-intensity, long-term, high-cognitive load work, especially repeated work at night or across the day and night, will have obvious adverse effects on the workers' physiological rhythms and attention, and can easily lead to fatigue. In severe cases, it will endanger work safety and even cause accidents, causing irreparable losses and harm to society and individuals. Therefore, early warning systems and methods for avoiding fatigue work are of great significance to both society and individuals.

[0003] The existing fatigue work warning system and method collects the working images of the workers through infrared cameras, detects the face images through the MTCNNN face detection method, and then segments the face images through the differential image adaptive threshold segmentation method to obtain eye images, and then extracts and tracks the features of the eye images through the PCA principal component analysis method and the Kalman filter eye tracking method, and finally calculates the ratio of the number of eye closure frames to the total number of recognized eye image frames through the feature extraction and tracking of the eye images to determine and warn the fatigue state of the workers. However, it is difficult to obtain the effective warning data of the workers accurately and timely by obtaining the data of the eye state of the workers for judgment and warning. Summary of the invention

[0004] The present invention provides an early warning system and method for avoiding fatigue work, which solves the problem in the prior art that it is difficult to accurately and timely obtain effective early warning data of operators, and realizes accurate early warning of the fatigue state of operators.

[0005] In order to solve the above-mentioned invention object, the technical solution provided by the present invention is as follows:

[0006] On the one hand, an embodiment of the present invention provides an early warning system for avoiding fatigue work, comprising: a data collection module, an eye state data accuracy assessment module, a physiological state data accuracy assessment module, an early warning data accuracy assessment module and an analysis module; the data collection module collects early warning data obtained by two sets of identical equipment at two preset positions at the same time by the operator; the eye state data accuracy assessment module obtains two sets of eye state assessment data and image data in the early warning data and analyzes them to obtain eye state assessment data accuracy indicators and image quality analysis indicators, analyzes the accuracy of the two sets of eye state assessment data according to the eye state assessment data accuracy indicators, and selects an image quality analysis indicator for the early warning system according to the image quality analysis indicator. Accurate data for eye state assessment; the physiological state data accuracy assessment module: obtains two groups of physiological state assessment data in the early warning data and analyzes them to obtain the physiological state assessment data accuracy index and the physiological state assessment data selection index, analyzes the accuracy of the two groups of physiological state assessment data according to the physiological state assessment data accuracy index, and determines the valid data for physiological state assessment according to the physiological state assessment data selection index; the early warning data accuracy assessment module: comprehensively integrates the eye state assessment data accuracy index and the physiological state assessment data accuracy index to obtain the early warning data accuracy assessment index of the operator; the analysis module: analyzes the accuracy of the early warning data according to the early warning data accuracy assessment index and issues an early warning.

[0007] Optionally, the early warning data includes two groups of eye state assessment data and two groups of physiological state assessment data; the two groups of eye state assessment data include: a first group of eye state assessment data and a second group of eye state assessment data, the first group of eye state assessment data includes a first group of blinking frequency, a first group of blinking duration, a first group of eye movement speed, a first group of pupil diameter and a first group of eyelid closure duration data, and the second group of eye state assessment data includes a second group of blinking frequency, a second group of blinking duration, a second group of eye movement speed, a second group of pupil diameter and a second group of eyelid closure duration data; the two groups of physiological state assessment data include a first group of physiological state assessment data and a second group of physiological state assessment data, the first group of physiological state assessment data includes a first group of heart rate, a first group of skin conductivity, a first group of brain wave alpha wave number and a first group of blood oxygen saturation, and the second group of physiological state assessment data includes a second group of heart rate, a second group of skin conductivity, a second group of brain wave alpha wave number and a second group of blood oxygen saturation.

[0008] Optionally, the specific method for obtaining the accuracy index of the eye state assessment data is:

[0009]

[0010] Wherein, α is represented by the accuracy index of eye state assessment data, f″ is represented by the second group blink frequency, t″ is represented by the second group blink duration, s″ is represented by the second group eye movement speed, d″ is represented by the second group pupil diameter, T″ is represented by the second group eyelid closure duration, f′ is represented by the first group blink frequency, t′ is represented by the first group blink duration, s′ is represented by the first group eye movement speed, d′ is represented by the first group pupil diameter, T′ is represented by the first group eyelid closure duration, and e is a natural constant.

[0011] Optionally, the specific analysis process of the eye state assessment data accuracy index is: obtaining a preset eye state assessment data accuracy index threshold, obtaining the eye state assessment data accuracy index, comparing the obtained eye state assessment data accuracy index with the eye state assessment data accuracy index threshold, when the eye state assessment data accuracy index is greater than the eye state assessment data accuracy index threshold, the eye state assessment data accuracy index is in an abnormal range; when the eye state assessment data accuracy index is less than or equal to the eye state assessment data accuracy index threshold, the eye state assessment data accuracy index is in a normal range.

[0012] Optionally, the specific process of analyzing the accuracy of the two sets of eye state assessment data according to the eye state assessment data accuracy index is as follows: when the eye state assessment data accuracy index is within the normal range, it means that the two sets of eye state assessment data obtained are accurate, and the two sets of eye state assessment data at this time can be used to assess the eye state and issue an early warning; when the eye state assessment data accuracy index is within the abnormal range, it means that the two sets of eye state assessment data obtained at this time are inaccurate; when the eye state assessment data accuracy index is within the abnormal range, the eye state image data is acquired for analysis to obtain the image quality analysis index, and the valid data for eye state assessment is determined according to the image quality analysis index.

[0013] Optionally, the specific analysis process of the physiological state assessment data accuracy index is: obtaining a preset physiological state assessment data accuracy index threshold, obtaining the physiological state assessment data accuracy index, comparing the obtained physiological state assessment data accuracy index with the physiological state assessment data accuracy index threshold, when the physiological state assessment data accuracy index is greater than the physiological state assessment data accuracy index threshold, the physiological state assessment data accuracy index is in an abnormal range; when the physiological state assessment data accuracy index is less than or equal to the physiological state assessment data accuracy index threshold, the physiological state assessment data accuracy index is in a normal range.

[0014] Optionally, the specific process of analyzing the accuracy of the two groups of physiological state assessment data according to the physiological state assessment data accuracy index is: when the physiological state assessment data accuracy index is in a normal range, it means that the two groups of physiological state assessment data obtained are accurate; when the physiological state assessment data accuracy index is in an abnormal range, it means that the two groups of physiological state assessment data obtained are inaccurate; when the physiological state assessment data accuracy index is in an abnormal range, a physiological state assessment data selection index is obtained, and valid data for physiological state assessment is determined according to the physiological state assessment data selection index.

[0015] Optionally, the specific analysis process of the warning data accuracy assessment indicator is: obtain a preset warning data accuracy assessment indicator threshold, obtain the warning data accuracy assessment indicator, compare the obtained warning data accuracy assessment indicator with the warning data accuracy assessment indicator threshold, when the warning data accuracy assessment indicator is greater than the warning data accuracy assessment indicator threshold, the warning data accuracy assessment indicator is in an abnormal range; when the warning data accuracy assessment indicator is less than or equal to the warning data accuracy assessment indicator threshold, the warning data accuracy assessment indicator is in a normal range.

[0016] Optionally, the specific process of analyzing the accuracy of the warning data and issuing a warning based on the warning data accuracy evaluation index is as follows: when the warning data accuracy evaluation index is within a normal range, it means that the obtained warning data is accurate and the obtained warning data can be used for warning; when the warning data accuracy evaluation index is within an abnormal range, it means that the obtained warning data is inaccurate and the warning data will not be used to warn the fatigue status of the operator.

[0017] On the other hand, an embodiment of the present invention also provides an early warning method for avoiding fatigue work, comprising the following steps: collecting early warning data obtained by the operator at two preset positions by two groups of identical equipment at the same time; obtaining two groups of eye state assessment data and image data in the early warning data and analyzing them to obtain an eye state assessment data accuracy index and an image quality analysis index, analyzing the accuracy of the two groups of eye state assessment data according to the eye state assessment data accuracy index, and selecting accurate data for eye state assessment according to the image quality analysis index; obtaining two groups of physiological state assessment data in the early warning data and analyzing them to obtain a physiological state assessment data accuracy index and a physiological state assessment data selection index, analyzing the accuracy of the two groups of physiological state assessment data according to the physiological state assessment data accuracy index, and determining valid data for physiological state assessment according to the physiological state assessment data selection index; obtaining the eye state assessment data accuracy index and the physiological state assessment data accuracy index to obtain the operator's early warning data accuracy assessment index; analyzing the accuracy of the warning data according to the early warning data accuracy assessment index and issuing an early warning.

[0018] Compared with the prior art, the above technical solution has at least the following beneficial effects:

[0019] 1. By obtaining two sets of eye state assessment data of the operator, the accuracy index of the eye state assessment data of the operator is obtained and the two sets of eye state assessment data of the operator are evaluated, and the two sets of eye state image data of the operator are obtained and analyzed to obtain the image quality analysis index, and the effective data is selected according to the image quality analysis index, so that accurate data can be obtained in a timely and effective manner, and then two sets of physiological state assessment data of the operator are obtained, and the accurate data for eye state assessment is determined according to the physiological state assessment data selection index, so as to obtain the accuracy index of the physiological state assessment data of the operator and analyze the two sets of physiological state assessment data of the operator, and finally the accuracy index of the eye state assessment data and the accuracy index of the physiological state assessment data are comprehensively used to obtain the accuracy assessment index of the early warning data, and the accuracy of the early warning data is accurately analyzed according to the early warning data accuracy assessment index, and then an early warning is issued according to the accurate early warning data, which effectively solves the problem that it is difficult to accurately and timely obtain effective early warning data of the operator in the prior art.

[0020] 2. By obtaining two sets of eye condition assessment data of the operating personnel, the accuracy index of the eye condition assessment data of the operating personnel is obtained and the accuracy of the two sets of eye condition assessment data of the operating personnel is analyzed, thereby accurately analyzing the accuracy of the two sets of eye condition assessment data.

[0021] 3. By obtaining two sets of physiological status assessment data of the operating personnel, the accuracy index of the physiological status assessment data is obtained and the accuracy of the two sets of physiological status assessment data of the operating personnel is analyzed, thereby accurately analyzing the accuracy of the two sets of physiological status assessment data of the operating personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 A diagram of an early warning system for avoiding fatigue work provided by the present invention;

[0024] Figure 2 A curve chart of the early warning data accuracy evaluation index provided by the present invention;

[0025] Figure 3 A graph showing a comprehensive evaluation index curve of fatigue status of operators provided by the present invention;

[0026] Figure 4 A flow chart of an early warning method for avoiding fatigue work provided by the present invention. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] Unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. Similarly, words such as "one", "one" or "the" do not indicate a quantitative limitation, but indicate the existence of at least one. Words such as "include" or "comprise" mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Words such as "connect" or "connected" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect.

[0029] It should be noted that the terms "up", "down", "left", "right", "front", "back", etc. used in the present invention are only used to indicate relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0030] In view of the problem that it is difficult to accurately and timely obtain effective early warning data of operators, the present invention provides a system and method that can accurately analyze the fatigue state of operators and issue early warnings.

[0031] like Figure 1As shown, a diagram of an early warning system for avoiding fatigue work provided by an embodiment of the present invention includes: a data collection module, an eye state data accuracy evaluation module, a physiological state data accuracy evaluation module, an early warning data accuracy evaluation module and an analysis module; the data collection module collects early warning data obtained by two sets of identical devices at two preset positions at the same time by the operator; the eye state data accuracy evaluation module obtains two sets of eye state evaluation data and image data in the early warning data and analyzes them to obtain eye state evaluation data accuracy indicators and image quality analysis indicators, analyzes the accuracy of the two sets of eye state evaluation data according to the eye state evaluation data accuracy indicators, and selects the image quality analysis indicators according to the image quality analysis indicators. Accurate data for eye state assessment; Physiological state data accuracy assessment module: obtain two groups of physiological state assessment data in the warning data and analyze them to obtain the physiological state assessment data accuracy index and the physiological state assessment data selection index, analyze the accuracy of the two groups of eye state assessment data according to the physiological state assessment data accuracy index, and determine the accurate data for eye state assessment according to the physiological state assessment data selection index; Warning data accuracy assessment module: comprehensively integrate the eye state assessment data accuracy index and the physiological state assessment data accuracy index to obtain the warning data accuracy assessment index of the operator; Analysis module: analyze the accuracy of the warning data according to the warning data accuracy assessment index and issue a warning.

[0032] In this embodiment, it should be noted that the two sets of eye state assessment data are two sets of eye state assessment data obtained by the same devices placed at different positions, wherein the first set of eye state assessment data is obtained by the first set of devices at a preset first position, and the second set of eye state assessment data is obtained by the second set of devices at a preset second position. The two sets of physiological state assessment data are two sets of physiological state assessment data obtained by the same devices placed at different positions, wherein the first set of physiological state assessment data is obtained by placing the first set at the preset position, and the second set of physiological state assessment data is obtained by placing the second set at the preset position. It should be noted that the preset position of the first group and the preset position of the second group are two similar sets of positions.

[0033] Among them, the warning data include two groups of eye state assessment data and two groups of physiological state assessment data. The two groups of eye state assessment data include: a first group of eye state assessment data and a second group of eye state assessment data. The first group of eye state assessment data includes a first group of blinking frequency, a first group of blinking duration, a first group of eye movement speed, a first group of pupil diameter and a first group of eyelid closure duration data. The second group of eye state assessment data includes a second group of blinking frequency, a second group of blinking duration, a second group of eye movement speed, a second group of pupil diameter and a second group of eyelid closure duration data; the two groups of physiological state assessment data include a first group of physiological state assessment data and a second group of physiological state assessment data. The first group of physiological state assessment data includes a first group of heart rate, a first group of skin conductivity, a first group of brain wave alpha wave number and a first group of blood oxygen saturation. The second group of physiological state assessment data includes a second group of heart rate, a second group of skin conductivity, a second group of brain wave alpha wave number and a second group of blood oxygen saturation.

[0034] In this embodiment, the first group of blinking frequency, the first group of blinking duration, the first group of eye movement speed, the first group of pupil diameter, the first group of eyelid closure duration data, the second group of blinking frequency, the second group of blinking duration, the second group of eye movement speed, the second group of pupil diameter and the second group of eyelid closure duration data can be obtained through an eye tracker; the first group of heart rate and the second group of heart rate can be obtained by placing an optical sensor on a steering wheel or a seat, the first group of skin conductivity and the second group of skin conductivity can be obtained by installing a non-contact sensor, such as a capacitive sensor, on a steering wheel or a seat, the first group of brain wave alpha wave number and the second group of brain wave alpha wave number can be obtained through wireless dry electrode technology, and the first group of blood oxygen saturation and the second group of blood oxygen saturation can be obtained through a pulse oximeter clipped on the earlobe.

[0035] Among them, the specific method for obtaining the accuracy index of eye status assessment data is as follows:

[0036]

[0037] Wherein, α is represented by the accuracy index of eye state assessment data, f″ is represented by the second group blink frequency, t″ is represented by the second group blink duration, s″ is represented by the second group eye movement speed, d″ is represented by the second group pupil diameter, T″ is represented by the second group eyelid closure duration, f′ is represented by the first group blink frequency, t′ is represented by the first group blink duration, s′ is represented by the first group eye movement speed, d′ is represented by the first group pupil diameter, T′ is represented by the first group eyelid closure duration, and e is a natural constant.

[0038] In this embodiment, f″ represents the number of blinks per unit time in the second set of data, t″ represents the time length from the start to the end of a blink in the second set of data, s″ represents the speed of the eyeball moving in the field of vision in the second set of data, d″ represents the size of the pupil diameter in the second set of data, T″ represents the time length that the eyelid is completely closed in the second set of data, f′ represents the number of blinks per unit time in the first set of data, t′ represents the time length from the start to the end of a blink in the initial state of the operator in the first set of data, s′ represents the speed of the eyeball moving in the field of vision in the initial state of the operator in the first set of data, d′ represents the size of the pupil diameter in the initial state of the operator in the first set of data, and T′ represents the time length that the eyelid is completely closed in the initial state of the operator in the first set of data.

[0039] In this embodiment, the accuracy of the data obtained by evaluating the first set of eye state assessment data and the second set of eye state assessment data is comprehensively evaluated. The closer the first set of eye state assessment data is to the corresponding second set of eye state assessment data, for example, the closer the first set of blinking frequency is to the second set of blinking frequency, the more accurate the two sets of eye state assessment data obtained are. The slower the first set of blinking frequency is, the longer the blinking duration and the eyelid closure duration are, and the slower the first set of eye movement speed is. This embodiment uses logarithmic operations for nonlinear transformation, which smoothes the influence of outliers and is beneficial to improving the accuracy and effectiveness of the eye state assessment data accuracy indicators.

[0040] Among them, the specific analysis process of the eye state assessment data accuracy index is: obtain a preset eye state assessment data accuracy index threshold, obtain the eye state assessment data accuracy index, compare the obtained eye state assessment data accuracy index with the eye state assessment data accuracy index threshold, when the eye state assessment data accuracy index is greater than the eye state assessment data accuracy index threshold, the eye state assessment data accuracy index is in an abnormal range; when the eye state assessment data accuracy index is less than or equal to the eye state assessment data accuracy index threshold, the eye state assessment data accuracy index is in a normal range.

[0041] In this embodiment, the preset eye state assessment data accuracy index threshold is α1, α1=1.07. When the eye state assessment data accuracy index is less than or equal to the eye state assessment data accuracy index threshold, that is, α≤α1, it indicates that the eye state assessment data accuracy index is in the normal range, that is, the two sets of eye state assessment data obtained are relatively accurate; when in other situations, that is, α>α1, it indicates that the eye state assessment data accuracy index is in the abnormal range, that is, the two sets of eye state assessment data obtained have a large deviation, that is, there are problems with the two sets of eye state assessment data obtained, and the images in the data acquisition process are analyzed to obtain a set of eye state assessment data with relatively good image quality for early warning.

[0042] Among them, the specific process of analyzing the accuracy of two sets of eye state assessment data according to the eye state assessment data accuracy index is: when the eye state assessment data accuracy index is in the normal range, it means that the two sets of eye state assessment data obtained are accurate, and the two sets of eye state assessment data at this time can be used to assess the eye state and issue an early warning; when the eye state assessment data accuracy index is in the abnormal range, it means that the two sets of eye state assessment data obtained at this time are inaccurate; when the eye state assessment data accuracy index is in the abnormal range, the eye state image data is obtained for analysis to obtain the image quality analysis index, and the valid data for eye state assessment is determined according to the image quality analysis index.

[0043] In this embodiment, when the accuracy index of the eye state assessment data is in the normal range, it means that the difference between the first group of eye state assessment data and the second group of eye state assessment data is small, that is, the two groups of eye state assessment data obtained at this time are highly reliable. At this time, the average value of the first group of eye state assessment data and the corresponding second group of eye state assessment data can be obtained as the eye state assessment data for evaluating the eye state of the operator. For example, the average value of the first group of blinking frequencies and the second group of blinking frequencies is obtained as the blinking frequency used in the early warning. The same is true for other data used in the early warning. When the accuracy index of the eye state assessment data is in the abnormal range, it means that the difference between the first group of eye state assessment data and the second group of eye state assessment data is large, that is, the two groups of eye state assessment data obtained at this time have certain problems. The data problems obtained at this time may be caused by multiple reasons. At this time, the images in the data acquisition process are analyzed to obtain a group of eye state assessment data with relatively good image quality for early warning.

[0044] In this embodiment, as described above, two groups of images are generated when an eye tracker is used to obtain two groups of eye state assessment data. When the accuracy index of the eye state assessment data is in an abnormal range, relevant data of the two groups of images are obtained to evaluate the quality of the two groups of images, and the group of eye state assessment data with better image quality is obtained as the eye state assessment data used in fatigue state warning. The method for obtaining the image quality analysis index is: obtaining the weight ratio of the preset image resolution, image pixel density, image contrast, and image signal-to-noise ratio in the image quality analysis index from a database;

[0045]

[0046] Wherein, ε represents the image quality analysis index, A represents the image resolution, A1 represents the standard value of image resolution, Q represents the image pixel density, Q1 represents the standard value of image pixel density, G represents the image contrast, G1 represents the standard value of image contrast, F represents the image signal-to-noise ratio, F1 represents the standard value of image signal-to-noise ratio, h1 represents the weight ratio of image resolution in the image quality analysis index, h2 represents the weight ratio of image pixel density in the image quality analysis index, h3 represents the weight ratio of image contrast in the image quality analysis index, and h4 represents the weight ratio of image signal-to-noise ratio in the image quality analysis index.

[0047] In this embodiment, the image resolution, image pixel density, image contrast, and image signal-to-noise ratio can be obtained through image analysis software, and the standard value of image resolution, image pixel density, image contrast, and image signal-to-noise ratio can be obtained through a database. In this embodiment, h1, h2, h3, and h4 are respectively the influence weight ratios corresponding to the preset image resolution, image pixel density, image contrast, and image signal-to-noise ratio obtained from the preset database, and are used to represent the numerical values ​​of the influence of the image resolution, image pixel density, image contrast, and image signal-to-noise ratio on the image quality analysis index. When used, the influence weight ratios corresponding to the image resolution, image pixel density, image contrast, and image signal-to-noise ratio can be directly obtained from the preset database.

[0048] In this embodiment, image quality analysis indicators of a first group of images generated by a first group of eye state assessment data and image quality analysis indicators of a second group of images generated by a second group of eye state assessment data are obtained. The image quality analysis indicators of the first group of images are obtained by substituting the image resolution, image pixel density, image contrast, and image signal-to-noise ratio of the first group of images into the above formula. Similarly, the image quality analysis indicators of the second group of images can be obtained. The image quality analysis indicators of the first group of images are compared with the image quality analysis indicators of the second group of images. The group of image data with a smaller image quality analysis indicator is closer to the image standard data, that is, the group of image data with a smaller image quality analysis indicator is better, that is, the group of eye state assessment data with a smaller image quality analysis indicator is selected as the eye state assessment data used in fatigue state warning.

[0049] Among them, the specific analysis process of the physiological state assessment data accuracy index is: obtain a preset physiological state assessment data accuracy index threshold, obtain the physiological state assessment data accuracy index, compare the obtained physiological state assessment data accuracy index with the physiological state assessment data accuracy index threshold, when the physiological state assessment data accuracy index is greater than the physiological state assessment data accuracy index threshold, the physiological state assessment data accuracy index is in an abnormal range; when the physiological state assessment data accuracy index is less than or equal to the physiological state assessment data accuracy index threshold, the physiological state assessment data accuracy index is in a normal range.

[0050] In this embodiment, the specific method for obtaining the accuracy index of the physiological status assessment data is:

[0051]

[0052] Wherein, γ represents the accuracy index of physiological state assessment data, a″ represents the heart rate of the second group, c″ represents the skin conductivity of the second group, y″ represents the number of α waves of the second group of brain waves, u″ represents the blood oxygen saturation of the second group, a′ represents the initial heart rate of the first group, c′ represents the initial skin conductivity of the first group, y′ represents the number of α waves of the first group of initial brain waves, and u′ represents the blood oxygen saturation of the first group.

[0053] In this embodiment, a″ represents the number of heart beats per unit time in the second group of data, c″ represents the conductivity of the skin surface in the second group of data, y″ represents the number of alpha waves in the electrical activity of the cerebral cortex nerve cells in the second group of data, u″ represents the percentage of oxygen content of hemoglobin in the blood in the second group of data, a′ represents the number of heart beats per unit time of the operator in the first group of data, c′ represents the conductivity of the skin surface of the operator in the first group of data, y′ represents the number of alpha waves in the electrical activity of the cerebral cortex nerve cells of the operator in the first group of data, and u′ represents the percentage of oxygen content of hemoglobin in the blood of the operator in the first group of data.

[0054] In this embodiment, the accuracy of the physiological state assessment data obtained by comprehensively evaluating the first set of physiological state assessment data and the second set of physiological state assessment data is evaluated. The smaller the difference between the first set of physiological state assessment data and the second set of physiological state assessment data, the more accurate the two sets of physiological state assessment data obtained are, and the more accurate the warning of the physiological state of the operator based on the two sets of physiological state assessment data is. This embodiment uses square root operation for nonlinear transformation, so that the accuracy index of the obtained physiological state assessment data is more explanatory.

[0055] In this embodiment, the preset physiological state assessment data accuracy index threshold is γ1, γ1=1.10. When the physiological state assessment data accuracy index is less than or equal to the physiological state assessment data accuracy index threshold, that is, γ≤γ1, it means that the physiological state assessment data accuracy index is in the normal range, that is, the two sets of physiological state assessment data obtained are accurate; when in other situations, that is, γ>γ1, it means that the physiological state assessment data accuracy index is in the abnormal range, that is, the two sets of physiological state assessment data obtained are inaccurate.

[0056] Among them, the specific process of analyzing the accuracy of the two groups of physiological state assessment data according to the physiological state assessment data accuracy index is: when the physiological state assessment data accuracy index is in the normal range, it means that the two groups of physiological state assessment data obtained are accurate; when the physiological state assessment data accuracy index is in the abnormal range, it means that the two groups of physiological state assessment data obtained are inaccurate; when the physiological state assessment data accuracy index is in the abnormal range, the physiological state assessment data selection index is obtained, and the valid data for physiological state assessment is determined according to the physiological state assessment data selection index.

[0057] In this embodiment, when the accuracy index of the physiological state assessment data is within the normal range, it means that the difference between the first group of physiological state assessment data and the second group of physiological state assessment data is small, that is, the accuracy of the two groups of physiological state assessment data obtained at this time is high, that is, the two groups of physiological state assessment data obtained at this time can be used to evaluate the physiological state of the operator. At this time, the average value of the first group of physiological state assessment data and the corresponding second group of physiological state assessment data can be taken as the physiological state assessment data for evaluating the physiological state of the operator. For example, the average value of the first group of heart rates and the second group of heart rates can be obtained as the heart rate used in the early warning, and the same is true for other data used in the early warning. When the accuracy index of the physiological state assessment data is within the abnormal range, it means that the difference between the first group of physiological state assessment data and the second group of physiological state assessment data is large, that is, the two groups of physiological state assessment data obtained at this moment are not suitable for use in the early warning of the fatigue state of the operator, which will result in inaccurate early warning of the physiological state of the operator. Then, the physiological state assessment data selection index of the two groups of physiological state assessment data is obtained to select a relatively better group of physiological state assessment data for early warning.

[0058] In this embodiment, when the accuracy index of the physiological state assessment data is in an abnormal range, the two groups of physiological state assessment data are compared with the initial physiological data of the operator respectively to obtain the physiological state assessment data selection index, and the physiological state assessment data used in the fatigue state warning is selected according to the physiological state assessment data selection index of the first group of physiological state assessment data and the physiological state assessment data selection index of the second group of physiological state assessment data. The method for obtaining the physiological state assessment data selection index is:

[0059]

[0060] In this embodiment, λ represents the physiological status assessment data selection index, a′ represents the number of heart beats per unit time of the operator in the first set of data, c′ represents the conductivity of the skin surface of the operator in the first set of data, y′ represents the number of α waves in the electrical activity of the cerebral cortex nerve cells of the operator in the first set of data, u′ represents the percentage of oxygen content of hemoglobin in the blood of the operator in the first set of data, a1 represents the initial heart rate of the operator, c1 represents the initial skin conductivity of the operator, y1 represents the number of initial brain wave α waves of the operator, and u1 represents the initial blood oxygen saturation of the operator.

[0061] In this embodiment, obtaining the physiological data of the operator in the initial waking state when performing the operation is to obtain the initial physiological data of the operator, and the initial physiological data include initial heart rate, initial skin conductivity, initial number of brain wave alpha waves and initial blood oxygen saturation. The initial heart rate can be obtained by placing an optical sensor on the steering wheel or seat, the initial skin conductivity can be obtained by installing a non-contact sensor, such as a capacitive sensor on the steering wheel or seat, the initial number of brain wave alpha waves can be obtained by wireless dry electrode technology, and the initial blood oxygen saturation can be obtained by a pulse oximeter clipped to the earlobe.

[0062] In this embodiment, the first group of initial heart rate, the first group of initial skin conductivity, the first group of initial brain wave alpha wave number and the first group of blood oxygen saturation are compared with the initial heart rate, the initial skin conductivity, the initial brain wave alpha wave number and the initial blood oxygen saturation according to the above formula to obtain the physiological state assessment data selection index of the first group of physiological state assessment data, the second group of initial heart rate, the second group of initial skin conductivity, the second group of initial brain wave alpha wave number and the second group of blood oxygen saturation are compared with the initial heart rate, the initial skin conductivity, the initial brain wave alpha wave number and the initial blood oxygen saturation according to the above formula to obtain the physiological state assessment data selection index of the second group of physiological state assessment data, the physiological state assessment data selection index of the first group of physiological state assessment data is compared with the physiological state assessment data selection index of the second group of physiological state assessment data, the group of data with a smaller physiological state assessment data selection index is closer to the initial physiological data, and at this time, the group of data with a smaller physiological state assessment data selection index is selected as the physiological state assessment data used in fatigue state warning.

[0063] Among them, the specific analysis process of the warning data accuracy assessment indicator is: obtain the preset warning data accuracy assessment indicator threshold, obtain the warning data accuracy assessment indicator, compare the obtained warning data accuracy assessment indicator with the warning data accuracy assessment indicator threshold, when the warning data accuracy assessment indicator is greater than the warning data accuracy assessment indicator threshold, the warning data accuracy assessment indicator is in the abnormal range; when the warning data accuracy assessment indicator is less than or equal to the warning data accuracy assessment indicator threshold, the warning data accuracy assessment indicator is in the normal range.

[0064] In this embodiment, the specific method for obtaining the early warning data accuracy evaluation index is: obtaining the weight ratio of the preset eye state evaluation data accuracy index and the physiological state evaluation data accuracy index in the early warning data accuracy evaluation index from the database;

[0065]

[0066] Wherein, η represents the accuracy evaluation index of early warning data, α represents the accuracy index of eye state evaluation data, γ represents the accuracy index of physiological state evaluation data, k1 represents the weight ratio of eye state evaluation data accuracy index in early warning data accuracy evaluation index, k2 represents the weight ratio of physiological state evaluation data accuracy index in early warning data accuracy evaluation index, and e is a natural constant.

[0067] In this embodiment, k1 and k2 are respectively the influence weight ratios corresponding to the preset eye state assessment data accuracy index and the physiological state assessment data accuracy index obtained from the preset database, which are used to represent the numerical value of the influence degree of the eye state assessment data accuracy index and the physiological state assessment data accuracy index on the warning data accuracy assessment index. When used, the influence weight ratios corresponding to the eye state assessment data accuracy index and the physiological state assessment data accuracy index can be directly obtained from the preset database, and the corresponding relationship can be a preset mapping relationship. For example, the eye state assessment data accuracy index and the physiological state assessment data accuracy index and their corresponding weights form a mapping set. After the real-time eye state assessment data accuracy index and the physiological state assessment data accuracy index are input into the mapping set, the corresponding weight ratios corresponding to the eye state assessment data accuracy index and the physiological state assessment data accuracy index will be obtained. In this example, the value ranges of k1 and k2 are both [0, 1], and the sum of k1 and k2 is 1.

[0068] In this embodiment, the accuracy index of the eye state assessment data and the accuracy index of the physiological state assessment data are combined to perform a comprehensive assessment of the fatigue state of the operator. When the accuracy assessment index of the early warning data is in an abnormal range, it means that the obtained early warning data is inaccurate, which will have a certain impact on the early warning of the operator. The larger the accuracy index of the eye state assessment data and the accuracy index of the physiological state assessment data, the larger the early warning data accuracy assessment index, that is, the more inaccurate the early warning data of the operator. This embodiment uses inverse tangent to perform nonlinear transformation, so that the obtained early warning data accuracy assessment index is more reliable and effective.

[0069] In this embodiment, the preset warning data accuracy assessment index threshold is η1, η1=1.01. When the warning data accuracy assessment index is less than or equal to the warning data accuracy assessment index threshold, that is, η≤η1, it means that the warning data accuracy assessment index is in the normal range, which means that the obtained warning data is relatively accurate; when in other situations, that is, η>η1, it means that the warning data accuracy assessment index is in the abnormal range, which means that the obtained warning data accuracy is low.

[0070] like Figure 2As shown, it is a curve chart of the early warning data accuracy evaluation index provided by an embodiment of the present invention, with the eye state evaluation data accuracy index as the independent variable and the early warning data accuracy evaluation index as the dependent variable. Through the increase and decrease analysis of curve 1, curve 2 and curve 3, it can be known that when the eye state evaluation data accuracy index is larger, the early warning data accuracy evaluation index is larger; when the eye state evaluation data accuracy index is smaller, the early warning data accuracy evaluation index is smaller. The relevant data statistics of the early warning data accuracy evaluation index are shown in Table 1:

[0071] Table 1 Statistical table of relevant data on early warning data accuracy evaluation indicators

[0072]

[0073] It can be seen from Table 1 that for curve 1, curve 2 and curve 3, when the eye state assessment data accuracy index of curve 1, curve 2 and curve 3 is 1.00, when the physiological state assessment data accuracy index of curve 1, curve 2 and curve 3 is 0.80, 1.00 and 1.20 respectively, and other parameters are consistent, the early warning data accuracy assessment index of curve 1, curve 2 and curve 3 is 0.89, 0.94 and 0.98 respectively. It can be seen that when the eye state assessment data accuracy index is consistent with other parameters, when the physiological state assessment data accuracy index is larger, the early warning data accuracy assessment index is larger.

[0074] Among them, the specific process of analyzing the accuracy of the warning data and issuing a warning based on the warning data accuracy evaluation index is as follows: when the warning data accuracy evaluation index is within the normal range, it means that the obtained warning data is accurate and the obtained warning data can be used for warning; when the warning data accuracy evaluation index is within the abnormal range, it means that the obtained warning data is inaccurate, and the warning data will not be used to warn the fatigue status of the operator.

[0075] In this embodiment, when the accuracy index of the early warning data is in the normal range, it means that the early warning data of the operator obtained at the same time is relatively accurate. At this time, the average value of the corresponding data of the two groups of eye state evaluation data and the two groups of physiological state evaluation data in the early warning data can be taken as the early warning data used in the early warning process to evaluate the fatigue state of the operator. When the accuracy index of the early warning data is in the abnormal range, it means that the early warning data of the operator obtained at the same time is inaccurate, and there are certain problems with the early warning data obtained at this time. At this time, a relatively good group of eye state evaluation data in the two groups of eye state evaluation data can be selected according to the image quality analysis index as the eye state evaluation data used in the early warning of the fatigue state of the operator. Similarly, a relatively good group of physiological state evaluation data in the two groups of physiological state evaluation data can be selected according to the physiological state evaluation data selection index as the physiological state evaluation data used in the early warning of the fatigue state of the operator. At the same time, continue to obtain the next group of early warning data to evaluate the accuracy of the early warning data. When the accuracy index of the early warning data is in the abnormal range for five consecutive times, it means that the device for obtaining the early warning data is abnormal, and the device should be checked in time and replaced in time.

[0076] In this embodiment, the main process of issuing an early warning for the fatigue state of the operator is: collecting the initial state data of the operator; obtaining the eye state evaluation data of the operator, obtaining the eye fatigue state evaluation index based on the eye state evaluation data and the initial state data, analyzing the eye fatigue state of the operator according to the eye fatigue state evaluation index and issuing a first early warning; obtaining the physiological state evaluation data of the operator, obtaining the physiological fatigue state evaluation index of the operator according to the physiological state evaluation data and the initial state data, analyzing the physiological fatigue state of the operator according to the physiological fatigue state evaluation index and issuing a second early warning; obtaining the comprehensive evaluation index of the operator's fatigue state by combining the eye fatigue state evaluation index and the physiological fatigue state evaluation index; analyzing the overall fatigue state of the operator according to the comprehensive evaluation index of the operator's fatigue state and issuing three early warnings.

[0077] In this embodiment, the specific method for obtaining the eye fatigue state evaluation index is: obtaining the weight ratio of the preset blinking frequency, blinking duration, eye movement speed, pupil diameter and eyelid closure duration in the eye fatigue state evaluation index from the database;

[0078]

[0079] In the formula, β is the eye fatigue status evaluation index, f is the blinking frequency of the operator, t is the duration of the blinking of the operator, s is the eye movement speed of the operator, d is the pupil diameter of the operator, T is the eyelid closure duration of the operator, f1 is the initial blinking frequency of the operator, t1 is the initial blinking duration of the operator, s1 is the initial eye movement speed of the operator, d1 is the initial pupil diameter of the operator, T1 is the initial eyelid closure duration of the operator, and b1 is b4 represents the weight ratio of blinking frequency in the eye fatigue status assessment index, b5 represents the weight ratio of eyelid closure time in the eye fatigue status assessment index, and e is a natural constant. It should be noted that the data used in the above formula is accurate data obtained through analysis of eye status assessment data accuracy index.

[0080] In this embodiment, the initial blinking frequency, the initial blinking duration, the initial eye movement speed, the initial pupil diameter, the initial eyelid closure duration, the initial heart rate, the initial skin conductivity, the initial number of EEG alpha waves and the initial blood oxygen saturation are the initial state data of the operator. Obtaining the data of the operator in the initial awake state when performing the operation is to obtain the initial state data of the operator. The initial heart rate can be obtained by placing an optical sensor on the steering wheel or the seat, the initial skin conductivity can be obtained by installing a non-contact sensor, such as a capacitive sensor on the steering wheel or the seat, the initial number of EEG alpha waves can be obtained by wireless dry electrode technology, and the initial blood oxygen saturation can be obtained by a pulse oximeter clipped to the earlobe; the initial blinking frequency, the initial blinking duration, the initial eye movement speed, the initial pupil diameter and the initial eyelid closure duration are obtained by an eye tracker.

[0081] In this embodiment, f represents the number of blinks per unit time, t represents the time length from the start to the end of a blink, s represents the speed of the eyeball moving in the field of vision, d represents the size of the pupil diameter, T represents the length of time the eyelid is completely closed, f1 represents the number of blinks per unit time in the initial state of the operator, t1 represents the time length from the start to the end of a blink in the initial state of the operator, s1 represents the speed of the eyeball moving in the field of vision in the initial state of the operator, d1 represents the size of the pupil diameter in the initial state of the operator, and T1 represents the length of time the eyelid is completely closed in the initial state of the operator.

[0082] In this embodiment, b1, b2, b3, b4 and b5 are respectively the influence weight ratios of the preset blinking frequency, blinking duration, eye movement speed, pupil diameter and eyelid closure duration obtained from the preset database, which are used to represent the numerical values ​​of the influence of the blinking frequency, blinking duration, eye movement speed, pupil diameter and eyelid closure duration on the eye state assessment data. When used, the influence weight ratios of the blinking frequency, blinking duration, eye movement speed, pupil diameter and eyelid closure duration can be directly obtained from the preset database, and the corresponding relationship can be A mapping relationship is pre-set, for example, blinking frequency, blinking duration, eye movement speed, pupil diameter and eyelid closure duration and their corresponding weights form a mapping set. After the real-time blinking frequency, blinking duration, eye movement speed, pupil diameter and eyelid closure duration are input into the mapping set, the corresponding weight ratios of blinking frequency, blinking duration, eye movement speed, pupil diameter and eyelid closure duration will be obtained. In this example, the value ranges of b1, b2, b3, b4 and b5 are all [0, 1], and the sum of b1, b2, b3, b4 and b5 is 1.

[0083] In this embodiment, the blinking frequency, blinking duration, eye movement speed, pupil diameter and eyelid closure time are comprehensively used to evaluate the eye fatigue state of the operator, and the eye fatigue state of the operator is obtained for early warning. The closer the blinking frequency, blinking duration, eye movement speed, pupil diameter and eyelid closure time are to the initial blinking frequency, initial blinking duration, initial eye movement speed, initial pupil diameter and initial eyelid closure time, the more awake the operator is. The slower the blinking frequency, the longer the blinking duration and eyelid closure time, and the slower the eye movement speed. This embodiment uses logarithmic operation for nonlinear transformation, which smoothes the influence of outliers, which is beneficial to improving the accuracy and effectiveness of eye fatigue state evaluation indicators.

[0084] In this embodiment, the preset eye fatigue state assessment index threshold is β1, β1=1.07. When the eye fatigue state assessment index is less than or equal to the eye fatigue state assessment index threshold, that is, β≤β1, it indicates that the eye fatigue state assessment index is in the normal range, that is, the eye fatigue state of the operator is relatively light or the operator's eyes are not in a fatigue state; when in other situations, that is, β>β1, it indicates that the eye fatigue state assessment index is in an abnormal range, that is, the operator's eyes are in a fatigue state.

[0085] In this embodiment, when the eye fatigue status evaluation index is within the normal range, it means that the operator is relatively awake and no warning is given; when the eye fatigue status evaluation index is within the abnormal range, it means that the operator's eyes are in a relatively fatigued state. At this time, the first warning is given to the operator, and an obvious indicator light is used to remind the operator that the eyes are in a fatigued state. The operator is reminded to pay attention to eye rest through voice, and the operator is advised to relax his eyes appropriately in a place where the car can be parked.

[0086] In this embodiment, the specific method for obtaining the physiological fatigue state evaluation index is: obtaining the preset eye fatigue state evaluation index, heart rate, skin conductivity, number of alpha waves of brain waves and blood oxygen saturation in the physiological fatigue state evaluation index from the database;

[0087]

[0088] In the formula, δ represents the physiological fatigue state evaluation index, β represents the eye fatigue state evaluation index, a represents the heart rate of the operator, c represents the skin conductivity of the operator, y represents the number of alpha waves of the operator's brain wave, u represents the blood oxygen saturation of the operator, a1 represents the initial heart rate of the operator, c1 represents the initial skin conductivity of the operator, y1 represents the initial number of alpha waves of the operator's brain wave, u1 represents the initial blood oxygen saturation of the operator, d1 represents the weight ratio of heart rate in the physiological fatigue state evaluation index, d2 represents the weight ratio of heart rate in the physiological fatigue state evaluation index, d3 represents the weight ratio of heart rate in the physiological fatigue state evaluation index, d4 represents the weight ratio of heart rate in the physiological fatigue state evaluation index, d5 represents the weight ratio of heart rate in the physiological fatigue state evaluation index, and e is a natural constant. It should be noted that the data used in the above formula are accurate data obtained through the analysis of the accuracy index of physiological state evaluation data.

[0089] In this embodiment, a represents the number of heart beats per unit time, c represents the conductivity of the skin surface, y represents the number of alpha waves in the electrical activity of cerebral cortical nerve cells, u represents the percentage of oxygen content in hemoglobin in the blood, a1 represents the number of heart beats per unit time in the initial state of the operator, c1 represents the conductivity of the skin surface in the initial state of the operator, y1 represents the number of alpha waves in the electrical activity of cerebral cortical nerve cells in the initial state of the operator, and u1 represents the percentage of oxygen content in hemoglobin in the blood in the initial state of the operator.

[0090] In this embodiment, d1, d2, d3, d4 and d5 are respectively the influence weight ratios corresponding to the preset eye fatigue state evaluation index, heart rate, skin conductivity, number of brain wave alpha waves and blood oxygen saturation obtained from the preset database, which are used to represent the numerical values ​​of the influence degree of the eye fatigue state evaluation index, heart rate, skin conductivity, number of brain wave alpha waves and blood oxygen saturation on the eye state evaluation data. When used, the influence weight ratios corresponding to the eye fatigue state evaluation index, heart rate, skin conductivity, number of brain wave alpha waves and blood oxygen saturation can be directly obtained from the preset database, and the corresponding relationship can be It is a pre-set mapping relationship. For example, the eye fatigue status assessment index, heart rate, skin conductivity, number of brain wave alpha waves and blood oxygen saturation and their corresponding weights form a mapping set. After the real-time eye fatigue status assessment index, heart rate, skin conductivity, number of brain wave alpha waves and blood oxygen saturation are input into the mapping set, the corresponding weight ratios of the eye fatigue status assessment index, heart rate, skin conductivity, number of brain wave alpha waves and blood oxygen saturation will be obtained. In this example, the value ranges of d1, d2, d3, d4 and d5 are all [0, 1], and the sum of d1, d2, d3, d4 and d5 is 1.

[0091] In this embodiment, the fatigue degree of the physiological state of the operator is evaluated by comprehensively considering the heart rate, skin conductivity, number of alpha waves of brain waves and blood oxygen saturation, and the physiological state of the operator is accurately evaluated. The closer the heart rate, skin conductivity, number of alpha waves of brain waves and blood oxygen saturation are to the initial heart rate, initial skin conductivity, initial number of alpha waves of brain waves and initial blood oxygen saturation, the closer the physiological state of the operator is to the physiological state when awake; the smaller the eye fatigue state evaluation index is, the closer the heart rate, skin conductivity, number of alpha waves of brain waves and blood oxygen saturation are to the corresponding data of the initial state of the operator. This embodiment uses exponential operation for nonlinear transformation, so that the obtained physiological fatigue state evaluation index is more explanatory.

[0092] In this embodiment, the preset physiological fatigue state assessment index threshold is δ1, δ1=1.11. When the physiological fatigue state assessment index is less than or equal to the physiological fatigue state assessment index threshold, that is, δ≤δ1, it means that the physiological fatigue state assessment index is in the normal range, which means that the operator's physiological state is relatively awake; when in other situations, that is, δ>δ1, it means that the physiological fatigue state assessment index is in the abnormal range, which means that the operator's physiological state is relatively tired.

[0093] In this embodiment, when the physiological fatigue state evaluation index is in the normal range, it means that the physiological state of the operator is closer to the physiological state when awake, and no secondary warning is issued. When the physiological fatigue state evaluation index is in the abnormal range, it means that the physiological state of the operator is in a fatigue state. At this time, a secondary warning is issued to the operator, and a voice warning is given to the operator that the physiological state is in a fatigue state, warning the operator that the physical condition is in a relatively tired state. At this time, the fatigue state of the operator is already greater than the fatigue state during the first warning, and it is recommended to replace the operator or take a rest in time.

[0094] In this embodiment, the specific method for obtaining the comprehensive evaluation index of the fatigue state of the operator is: obtaining the weight ratio of the preset eye fatigue state evaluation index and the physiological fatigue state evaluation index in the comprehensive evaluation index of the fatigue state of the operator from the database;

[0095]

[0096] Wherein, θ is the comprehensive evaluation index of fatigue state of operators, β is the evaluation index of eye fatigue state, δ is the evaluation index of physiological fatigue state, r1 is the weight ratio of eye fatigue state evaluation index in the comprehensive evaluation index of fatigue state of operators, r2 is the weight ratio of physiological fatigue state evaluation index in the comprehensive evaluation index of fatigue state of operators, and e is a natural constant.

[0097] In this embodiment, r1 and r2 are respectively the influence weight ratios corresponding to the preset eye fatigue state assessment indicators and the physiological fatigue state assessment indicators obtained from the preset database, which are used to represent the numerical values ​​of the influence degree of the eye fatigue state assessment indicators and the physiological fatigue state assessment indicators on the eye state assessment data. When used, the influence weight ratios corresponding to the eye fatigue state assessment indicators and the physiological fatigue state assessment indicators can be directly obtained from the preset database, and the corresponding relationship can be a preset mapping relationship. For example, the eye fatigue state assessment indicators and the physiological fatigue state assessment indicators and their corresponding weights form a mapping set. After the real-time eye fatigue state assessment indicators and the physiological fatigue state assessment indicators are input into the mapping set, the corresponding weight ratios corresponding to the eye fatigue state assessment indicators and the physiological fatigue state assessment indicators can be obtained. In this example, the value ranges of r1 and r2 are both [0, 1], and the sum of r1 and r2 is 1.

[0098] In this embodiment, the eye fatigue state assessment index and the physiological fatigue state assessment index are combined to perform a comprehensive assessment of the fatigue state of the operator. When the comprehensive assessment index of the operator's fatigue state is in an abnormal range, it means that the operator is in a fatigue state, which will have a certain impact on the operator's judgment and operation. The larger the eye fatigue state assessment index and the physiological fatigue state assessment index, the larger the comprehensive assessment index of the operator's fatigue state, that is, the greater the operator's fatigue level. This embodiment uses an inverse sine function for nonlinear transformation, so that the obtained comprehensive assessment index of the operator's fatigue state is more reliable and effective.

[0099] In this embodiment, the preset threshold value of the comprehensive evaluation index of the fatigue state of the operator is θ1, θ1=1.77. When the comprehensive evaluation index of the fatigue state of the operator is less than or equal to the threshold value of the comprehensive evaluation index of the fatigue state of the operator, that is, θ≤θ1, it means that the comprehensive evaluation index of the fatigue state of the operator is in the normal range, that is, the operator's fatigue state is relatively mild; when it is in other situations, that is, θ>θ1, it means that the comprehensive evaluation index of the fatigue state of the operator is in the abnormal range, that is, the operator's fatigue state is relatively serious.

[0100] like Figure 3 As shown, it is a curve chart of comprehensive evaluation index of fatigue state of operators provided by an embodiment of the present invention, with eye fatigue state evaluation index as independent variable and comprehensive evaluation index of fatigue state of operators as dependent variable. Through the increase and decrease analysis of curve one, curve two and curve three, it can be known that when the eye fatigue state evaluation index is larger, the comprehensive evaluation index of fatigue state of operators is larger; when the eye fatigue state evaluation index is smaller, the comprehensive evaluation index of fatigue state of operators is smaller. The statistical table of relevant data of comprehensive evaluation index of fatigue state of operators is shown in Table 2:

[0101] Table 2 Statistics of comprehensive evaluation indicators of fatigue status of operators

[0102]

[0103] It can be seen from Table 1 that for curve 1, curve 2 and curve 3, when the eye fatigue state evaluation index of curve 1, curve 2 and curve 3 is 1.00, when the physiological fatigue state evaluation index of curve 1, curve 2 and curve 3 is 0.90, 1.00 and 1.10 respectively, and other parameters are consistent, the comprehensive evaluation index of operator fatigue state of curve 1, curve 2 and curve 3 is 1.40, 1.70 and 2.00 respectively. It can be seen that when the eye fatigue state evaluation index is consistent with other parameters, when the physiological fatigue state evaluation index is larger, the comprehensive evaluation index of operator fatigue state is larger.

[0104] In this embodiment, when the comprehensive evaluation index of the operator's fatigue state is within the normal range, it means that the operator's fatigue state is relatively light, and three warnings are not required at this time. When the comprehensive evaluation index of the operator's fatigue state is within the abnormal range, it means that the operator's fatigue level is relatively heavy. At this time, the operator's judgment and operation may have problems, and three warnings are required for the operator. At this time, the operator is already in an extremely fatigued state. The operator's continued operation may affect the operator's life and property safety, and the operator needs to be seriously warned. The operator needs to be given continuous voice warnings, and the operator can be kept awake by lowering the temperature in the car or opening the window, and compulsory measures are taken to limit the operator's driving speed. The operator is warned by voice to end the trip quickly and rest in time.

[0105] like Figure 4 As shown, it is a flow chart of an early warning method for avoiding fatigue work provided by an embodiment of the present invention. An early warning method for avoiding fatigue work provided by an embodiment of the present invention includes the following steps: collecting early warning data obtained by two groups of identical equipment at two preset positions at the same time by the operator; obtaining two groups of eye state assessment data and image data in the early warning data and analyzing them to obtain eye state assessment data accuracy index and image quality analysis index, analyzing the accuracy of the two groups of eye state assessment data according to the eye state assessment data accuracy index, and selecting accurate data for eye state assessment according to the image quality analysis index; obtaining two groups of physiological state assessment data in the early warning data and analyzing them to obtain physiological state assessment data accuracy index and physiological state assessment data selection index, analyzing the accuracy of the two groups of physiological state assessment data according to the physiological state assessment data accuracy index, and determining valid data for physiological state assessment according to the physiological state assessment data selection index; comprehensively obtaining the eye state assessment data accuracy index and the physiological state assessment data accuracy index to obtain the operator's early warning data accuracy assessment index; analyzing the accuracy of the early warning data according to the early warning data accuracy assessment index and issuing an early warning.

[0106] In summary, the embodiment of the present invention obtains two sets of eye state assessment data of the operator, thereby obtaining the accuracy index of the eye state assessment data of the operator and evaluating the two sets of eye state assessment data of the operator, and obtains two sets of eye state image data for analysis to obtain image quality analysis indicators, and selects valid data according to the image quality analysis indicators, so as to obtain accurate data in a timely and effective manner, and then obtains two sets of physiological state assessment data of the operator, obtains and determines the accurate data for eye state assessment according to the physiological state assessment data selection index, thereby obtaining the accuracy index of the physiological state assessment data of the operator and analyzing the two sets of physiological state assessment data of the operator, and finally obtains the accuracy index of the eye state assessment data and the accuracy index of the physiological state assessment data by comprehensively using the accuracy index of the eye state assessment data and the accuracy index of the physiological state assessment data, and accurately analyzes the accuracy of the warning data according to the accuracy evaluation index of the warning data, and then issues a warning based on the accurate warning data, which effectively solves the problem in the prior art that it is difficult to accurately and timely obtain effective warning data for the operator.

[0107] There are a few points to note:

[0108] (1) The drawings of the embodiments of the present invention only relate to the structures related to the embodiments of the present invention, and other structures may refer to the general design.

[0109] (2) For the sake of clarity, in the drawings used to describe the embodiments of the present invention, the thickness of the layers or regions is exaggerated or reduced, that is, these drawings are not drawn according to the actual scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or there may be intermediate elements.

[0110] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to obtain new embodiments.

[0111] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. An early warning system for avoiding fatigue work, characterized in that: include: Data collection module, eye status data accuracy assessment module, physiological status data accuracy assessment module, warning data accuracy assessment module and analysis module; The data collection module collects warning data obtained by operators at two preset locations at the same time using two sets of identical equipment; The eye state data accuracy assessment module: obtains two groups of eye state assessment data and eye state image data in the early warning data and analyzes them to obtain eye state assessment data accuracy indicators and image quality analysis indicators, analyzes the accuracy of the two groups of eye state assessment data according to the eye state assessment data accuracy indicators, and selects valid data for eye state assessment according to the image quality analysis indicators; The physiological state data accuracy assessment module: obtains two groups of physiological state assessment data in the early warning data and analyzes them to obtain the physiological state assessment data accuracy index and the physiological state assessment data selection index, analyzes the accuracy of the two groups of physiological state assessment data according to the physiological state assessment data accuracy index, and determines the valid data for physiological state assessment according to the physiological state assessment data selection index; The early warning data accuracy assessment module: comprehensively combines the eye state assessment data accuracy index and the physiological state assessment data accuracy index to obtain the early warning data accuracy assessment index of the operator; The analysis module analyzes the accuracy of the warning data and issues a warning based on the warning data accuracy evaluation index.

2. The early warning system for avoiding fatigue work according to claim 1, characterized in that: The early warning data includes two sets of eye status assessment data and two sets of physiological status assessment data; The two groups of eye state assessment data include: a first group of eye state assessment data and a second group of eye state assessment data, the first group of eye state assessment data includes a first group of blink frequency, a first group of blink duration, a first group of eye movement speed, a first group of pupil diameter and a first group of eyelid closure duration data, and the second group of eye state assessment data includes a second group of blink frequency, a second group of blink duration, a second group of eye movement speed, a second group of pupil diameter and a second group of eyelid closure duration data; The two groups of physiological state assessment data include a first group of physiological state assessment data and a second group of physiological state assessment data, the first group of physiological state assessment data includes a first group of heart rate, a first group of skin conductivity, a first group of brain wave alpha wave number and a first group of blood oxygen saturation, and the second group of physiological state assessment data includes a second group of heart rate, a second group of skin conductivity, a second group of brain wave alpha wave number and a second group of blood oxygen saturation.

3. The early warning system for avoiding fatigue work according to claim 1, characterized in that: The specific method for obtaining the accuracy index of the eye status assessment data is as follows: Wherein, α is represented by the accuracy index of eye state assessment data, f″ is represented by the second group blink frequency, t″ is represented by the second group blink duration, s″ is represented by the second group eye movement speed, d″ is represented by the second group pupil diameter, T″ is represented by the second group eyelid closure duration, f′ is represented by the first group blink frequency, t′ is represented by the first group blink duration, s′ is represented by the first group eye movement speed, d′ is represented by the first group pupil diameter, T′ is represented by the first group eyelid closure duration, and e is a natural constant.

4. The early warning system for avoiding fatigue work according to claim 3, characterized in that: The specific analysis process of the eye state assessment data accuracy index is as follows: obtaining a preset eye state assessment data accuracy index threshold, obtaining the eye state assessment data accuracy index, comparing the obtained eye state assessment data accuracy index with the eye state assessment data accuracy index threshold, and when the eye state assessment data accuracy index is greater than the eye state assessment data accuracy index threshold, the eye state assessment data accuracy index is in an abnormal range; When the eye state assessment data accuracy index is less than or equal to the eye state assessment data accuracy index threshold, the eye state assessment data accuracy index is within a normal range.

5. The early warning system for avoiding fatigue work according to claim 4, characterized in that: The specific process of analyzing the accuracy of the two groups of eye state assessment data according to the eye state assessment data accuracy index is as follows: when the eye state assessment data accuracy index is within the normal range, it means that the two groups of eye state assessment data obtained are accurate, and the two groups of eye state assessment data at this time can be used to assess the eye state and issue an early warning; when the eye state assessment data accuracy index is within the abnormal range, it means that the two groups of eye state assessment data obtained at this time are inaccurate; when the eye state assessment data accuracy index is within the abnormal range, the eye state image data is acquired for analysis to obtain the image quality analysis index, and the valid data for eye state assessment is determined according to the image quality analysis index.

6. The early warning system for avoiding fatigue work according to claim 1, characterized in that: The specific analysis process of the physiological state assessment data accuracy index is as follows: obtaining a preset physiological state assessment data accuracy index threshold, obtaining the physiological state assessment data accuracy index, comparing the obtained physiological state assessment data accuracy index with the physiological state assessment data accuracy index threshold, and when the physiological state assessment data accuracy index is greater than the physiological state assessment data accuracy index threshold, the physiological state assessment data accuracy index is in an abnormal range; When the physiological state assessment data accuracy index is less than or equal to the physiological state assessment data accuracy index threshold, the physiological state assessment data accuracy index is within a normal range.

7. The early warning system for avoiding fatigue work according to claim 6, characterized in that: The specific process of analyzing the accuracy of the two groups of physiological state assessment data according to the physiological state assessment data accuracy index is as follows: when the physiological state assessment data accuracy index is within the normal range, it means that the two groups of physiological state assessment data obtained are accurate; when the physiological state assessment data accuracy index is within the abnormal range, it means that the two groups of physiological state assessment data obtained are inaccurate; when the physiological state assessment data accuracy index is within the abnormal range, the physiological state assessment data selection index is obtained, and the valid data for physiological state assessment is determined according to the physiological state assessment data selection index.

8. The early warning system for avoiding fatigue work according to claim 1, characterized in that: The specific analysis process of the early warning data accuracy evaluation index is as follows: obtaining a preset early warning data accuracy evaluation index threshold, obtaining the early warning data accuracy evaluation index, comparing the obtained early warning data accuracy evaluation index with the early warning data accuracy evaluation index threshold, and when the early warning data accuracy evaluation index is greater than the early warning data accuracy evaluation index threshold, the early warning data accuracy evaluation index is in an abnormal range; When the early warning data accuracy assessment index is less than or equal to the early warning data accuracy assessment index threshold, the early warning data accuracy assessment index is within a normal range.

9. The early warning system for avoiding fatigue work according to claim 8, characterized in that: The specific process of analyzing the accuracy of the warning data and issuing a warning based on the warning data accuracy evaluation index is as follows: when the warning data accuracy evaluation index is within the normal range, it means that the obtained warning data is accurate and the obtained warning data can be used for warning; when the warning data accuracy evaluation index is within the abnormal range, it means that the obtained warning data is inaccurate and the warning data will not be used to warn the fatigue status of the operator.

10. A warning method for avoiding fatigue work, characterized in that: The following steps are involved: Collect warning data obtained by operators at two preset locations at the same time using two sets of identical equipment; Obtaining two sets of eye state assessment data and image data in the early warning data and analyzing them to obtain an eye state assessment data accuracy index and an image quality analysis index, analyzing the accuracy of the two sets of eye state assessment data according to the eye state assessment data accuracy index, and selecting accurate data for eye state assessment according to the image quality analysis index; Obtain two sets of physiological state assessment data in the early warning data and analyze them to obtain the physiological state assessment data accuracy index and the physiological state assessment data selection index, analyze the accuracy of the two sets of physiological state assessment data according to the physiological state assessment data accuracy index, and determine the valid data for physiological state assessment according to the physiological state assessment data selection index; The accuracy index of the early warning data of the operator is obtained by combining the accuracy index of the eye status assessment data and the accuracy index of the physiological status assessment data; Analyze the accuracy of warning data and issue warnings based on the warning data accuracy evaluation indicators.

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